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| 1 | 多电平变换器PWM控制技术研究现状综述及最新进展显示文摘近年来,多电平变换器成为高压、大功率电力电子系统应用领域的一个研究热点,而多电平脉宽调制PWM(pulse width modulation)控制方法是多电平变换器研究领域的核心问题之一。首先,阐述了3种载波PWM控制方法,并根据不同的控制指标介绍了各类优化载波PWM控制方法;其次,介绍了传统空间矢量脉宽调制SVPWM(space vector pulse width modulation)算法,并根据算法优化及控制指标详细介绍了不同坐标变换下的SVPWM,着重阐述了虚拟SVPWM及在其基础上衍生的各类优化虚拟空间矢量PWM在中点电压平衡控制中的应用;最后,介绍了n电平任意段多电平SVPWM与SPWM之间的本质联系,以及根据其统一性提出的优化PWM算法。 | 朱宏 李永东 王奎 QAMAR Muhammad Attique 徐晓娜 | 2021 | 电源学报2021，19，4： | 8 |
| 2 | Deep Rank-Based Average Pooling Network for Covid-19 Recognition显示文摘(Aim)To make a more accurate and precise COVID-19 diagnosis system,this study proposed a novel deep rank-based average pooling network(DRAPNet)model,i.e.,deep rank-based average pooling network,for COVID-19 recognition.(Methods)521 subjects yield 1164 slice images via the slice level selection method.All the 1164 slice images comprise four categories:COVID-19 positive;community-acquired pneumonia;second pulmonary tuberculosis;and healthy control.Our method firstly introduced an improved multiple-way data augmentation.Secondly,an n-conv rankbased average pooling module(NRAPM)was proposed in which rank-based pooling—particularly,rank-based average pooling(RAP)—was employed to avoid overfitting.Third,a novel DRAPNet was proposed based on NRAPM and inspired by the VGGnetwork.Grad-CAM was used to generate heatmaps and gave our AI model an explainable analysis.(Results)Our DRAPNet achieved a micro-averaged F1 score of 95.49%by 10 runs over the test set.The sensitivities of the four classes were 95.44%,96.07%,94.41%,and 96.07%,respectively.The precisions of four classes were 96.45%,95.22%,95.05%,and 95.28%,respectively.The F1 scores of the four classes were 95.94%,95.64%,94.73%,and 95.67%,respectively.Besides,the confusion matrix was given.(Conclusions)The DRAPNet is effective in diagnosing COVID-19 and other chest infectious diseases.The RAP gives better results than four other methods:strided convolution,l2-norm pooling,average pooling,and max pooling. | Shui-Hua Wang Muhammad Attique Khan Vishnuvarthanan Govindaraj Steven L.Fernandes Ziquan Zhu Yu-Dong Zhang | 2022 | Computers, Materials & Continua2022，，2： | 2 |
| 3 | A Hybrid Deep Learning Architecture for the Classification of Superhero Fashion Products:An Application for Medical-Tech Classification显示文摘Comic character detection is becoming an exciting and growing research area in the domain of machine learning.In this regard,recently,many methods are proposed to provide adequate performance.However,most of these methods utilized the custom datasets,containing a few hundred images and fewer classes,to evaluate the performances of their models without comparing it,with some standard datasets.This article takes advantage of utilizing a standard publicly dataset taken from a competition,and proposes a generic data balancing technique for imbalanced dataset to enhance and enable the in-depth training of the CNN.In addition,to classify the superheroes efficiently,a custom 17-layer deep convolutional neural network is also proposed.The computed results achieved overall classification accuracy of 97.9%which is significantly superior to the accuracy of competition’s winner. | Inzamam Mashood Nasir Muhammad Attique Khan Majed Alhaisoni Tanzila Saba Amjad Rehman Tassawar Iqbal | 2020 | Computer Modeling in Engineering & Sciences2020，，9： | 1 |
| 4 | Automated White Blood Cell Disease Recognition Using Lightweight Deep Learning显示文摘White blood cells(WBC)are immune system cells,which is why they are also known as immune cells.They protect the human body from a variety of dangerous diseases and outside invaders.The majority of WBCs come from red bone marrow,although some come from other important organs in the body.Because manual diagnosis of blood disorders is difficult,it is necessary to design a computerized technique.Researchers have introduced various automated strategies in recent years,but they still face several obstacles,such as imbalanced datasets,incorrect feature selection,and incorrect deep model selection.We proposed an automated deep learning approach for classifying white blood disorders in this paper.The data augmentation approach is initially used to increase the size of a dataset.Then,a Darknet-53 pre-trained deep learning model is used and finetuned according to the nature of the chosen dataset.On the fine-tuned model,transfer learning is used,and features engineering is done on the global average pooling layer.The retrieved characteristics are subsequently improved with a specified number of iterations using a hybrid reformed binary grey wolf optimization technique.Following that,machine learning classifiers are used to classify the selected best features for final classification.The experiment was carried out using a dataset of increased blood diseases imaging and resulted in an improved accuracy of over 99%. | Abdullah Alqahtani Shtwai Alsubai Mohemmed Sha Muhammad Attique Khan Majed Alhaisoni Syed Rameez Naqvi | 2023 | Computer Systems Science & Engineering2023，46，7： | 1 |
| 5 | Classification of Citrus Plant Diseases Using Deep Transfer Learning显示文摘In recent years,the field of deep learning has played an important role towards automatic detection and classification of diseases in vegetables and fruits.This in turn has helped in improving the quality and production of vegetables and fruits.Citrus fruits arewell known for their taste and nutritional values.They are one of the natural and well known sources of vitamin C and planted worldwide.There are several diseases which severely affect the quality and yield of citrus fruits.In this paper,a new deep learning based technique is proposed for citrus disease classification.Two different pre-trained deep learning models have been used in this work.To increase the size of the citrus dataset used in this paper,image augmentation techniques are used.Moreover,to improve the visual quality of images,hybrid contrast stretching has been adopted.In addition,transfer learning is used to retrain the pre-trainedmodels and the feature set is enriched by using feature fusion.The fused feature set is optimized using a meta-heuristic algorithm,the Whale Optimization Algorithm(WOA).The selected features are used for the classification of six different diseases of citrus plants.The proposed technique attains a classification accuracy of 95.7%with superior results when compared with recent techniques. | Muhammad Zia Ur Rehman Fawad Ahmed Muhammad Attique Khan Usman Tariq Sajjad Shaukat Jamal Jawad Ahmad Iqtadar Hussain | 2022 | Computers, Materials & Continua2022，，1： | 1 |
| 6 | Integrated CWT-CNN for Epilepsy Detection Using Multiclass EEG Dataset显示文摘Electroencephalography is a common clinical procedure to record brain signals generated by human activity.EEGs are useful in Brain controlled interfaces and other intelligent Neuroscience applications,but manual analysis of these brainwaves is complicated and time-consuming even for the experts of neuroscience.Various EEG analysis and classification techniques have been proposed to address this problem however,the conventional classification methods require identification and learning of specific EEG characteristics beforehand.Deep learning models can learn features from data without having in depth knowledge of data and prior feature identification.One of the great implementations of deep learning is Convolutional Neural Network(CNN)which has outperformed traditional neural networks in pattern recognition and image classification.Continuous Wavelet Transform(CWT)is an efficient signal analysis technique that presents the magnitude of EEG signals as timerelated Frequency components.Existing deep learning architectures suffer from poor performance when classifying EEG signals in the Time-frequency domain.To improve classification accuracy,we propose an integrated CWT and CNN technique which classifies five types of EEG signals using.We compared the results of proposed integrated CWT and CNN method with existing deep learning models e.g.,GoogleNet,VGG16,AlexNet.Furthermore,the accuracy and loss of the proposed integrated CWT and CNN method have been cross validated using Kfold cross validation.The average accuracy and loss of Kfold cross-validation for proposed integrated CWT and CNN method are,76.12%and 56.02%respectively.This model produces results on a publicly available dataset:Epilepsy dataset by UCI(Machine Learning Repository). | Sidra Naseem Kashif Javed Muhammad Jawad Khan Saddaf Rubab Muhammad Attique Khan Yunyoung Nam | 2021 | Computers, Materials & Continua2021，，10： | 1 |
| 7 | Segmentation and Classification of Stomach Abnormalities Using Deep Learning显示文摘An automated system is proposed for the detection and classification of GI abnormalities.The proposed method operates under two pipeline procedures:(a)segmentation of the bleeding infection region and(b)classification of GI abnormalities by deep learning.The first bleeding region is segmented using a hybrid approach.The threshold is applied to each channel extracted from the original RGB image.Later,all channels are merged through mutual information and pixel-based techniques.As a result,the image is segmented.Texture and deep learning features are extracted in the proposed classification task.The transfer learning(TL)approach is used for the extraction of deep features.The Local Binary Pattern(LBP)method is used for texture features.Later,an entropy-based feature selection approach is implemented to select the best features of both deep learning and texture vectors.The selected optimal features are combined with a serial-based technique and the resulting vector is fed to the Ensemble Learning Classifier.The experimental process is evaluated on the basis of two datasets:Private and KVASIR.The accuracy achieved is 99.8 per cent for the private data set and 86.4 percent for the KVASIR data set.It can be confirmed that the proposed method is effective in detecting and classifying GI abnormalities and exceeds other methods of comparison. | Javeria Naz Muhammad Attique Khan Majed Alhaisoni Oh-Young Song Usman Tariq Seifedine Kadry | 2021 | Computers, Materials & Continua2021，，10： | 1 |
| 8 | A Multilevel Deep Feature Selection Framework for Diabetic Retinopathy Image Classification显示文摘Diabetes or Diabetes Mellitus(DM)is the upset that happens due to high glucose level within the body.With the passage of time,this polygenic disease creates eye deficiency referred to as Diabetic Retinopathy(DR)which can cause a major loss of vision.The symptoms typically originate within the retinal space square in the form of enlarged veins,liquid dribble,exudates,haemorrhages and small scale aneurysms.In current therapeutic science,pictures are the key device for an exact finding of patients’illness.Meanwhile,an assessment of new medicinal symbolisms stays complex.Recently,Computer Vision(CV)with deep neural networks can train models with high accuracy.The thought behind this paper is to propose a computerized learning model to distinguish the key precursors of Dimensionality Reduction(DR).The proposed deep learning framework utilizes the strength of selected models(VGG and Inception V3)by fusing the extracated features.To select the most discriminant features from a pool of features,an entropy concept is employed before the classification step.The deep learning models are fit for measuring the highlights as veins,liquid dribble,exudates,haemorrhages and miniaturized scale aneurysms into various classes.The model will ascertain the loads,which give the seriousness level of the patient’s eye.The model will be useful to distinguish the correct class of seriousness of diabetic retinopathy pictures. | Farrukh Zia Isma Irum Nadia Nawaz Qadri Yunyoung Nam Kiran Khurshid Muhammad Ali Imran Ashraf Muhammad Attique Khan | 2022 | Computers, Materials & Continua2022，，2： | 1 |
| 9 | An Integrated Deep Learning Framework for Fruits Diseases Classification显示文摘:Agriculture has been an important research area in the field of image processing for the last five years.Diseases affect the quality and quantity of fruits,thereby disrupting the economy of a country.Many computerized techniques have been introduced for detecting and recognizing fruit diseases.However,some issues remain to be addressed,such as irrelevant features and the dimensionality of feature vectors,which increase the computational time of the system.Herein,we propose an integrated deep learning framework for classifying fruit diseases.We consider seven types of fruits,i.e.,apple,cherry,blueberry,grapes,peach,citrus,and strawberry.The proposed method comprises several important steps.Initially,data increase is applied,and then two different types of features are extracted.In the first feature type,texture and color features,i.e.,classical features,are extracted.In the second type,deep learning characteristics are extracted using a pretrained model.The pretrained model is reused through transfer learning.Subsequently,both types of features are merged using the maximum mean value of the serial approach.Next,the resulting fused vector is optimized using a harmonic threshold-based genetic algorithm.Finally,the selected features are classified using multiple classifiers.An evaluation is performed on the PlantVillage dataset,and an accuracy of 99%is achieved.A comparison with recent techniques indicate the superiority of the proposed method. | Abdul Majid Muhammad Attique Khan Majed Alhaisoni Muhammad Asfand Eyar Usman Tariq Nazar Hussain Yunyoung Nam Seifedine Kadry | 2022 | Computers, Materials & Continua2022，，4： | 1 |
| 10 | Gastrointestinal Tract Infections Classification Using Deep Learning显示文摘Automatic gastrointestinal(GI)tract disease recognition is an important application of biomedical image processing.Conventionally,microscopic analysis of pathological tissue is used to detect abnormal areas of the GI tract.The procedure is subjective and results in significant inter-/intraobserver variations in disease detection.Moreover,a huge frame rate in video endoscopy is an overhead for the pathological findings of gastroenterologists to observe every frame with a detailed examination.Consequently,there is a huge demand for a reliable computer-aided diagnostic system(CADx)for diagnosing GI tract diseases.In this work,a CADx was proposed for the diagnosis and classification of GI tract diseases.A novel framework is presented where preprocessing(LAB color space)is performed first;then local binary patterns(LBP)or texture and deep learning(inceptionNet,ResNet50,and VGG-16)features are fused serially to improve the prediction of the abnormalities in the GI tract.Additionally,principal component analysis(PCA),entropy,and minimum redundancy and maximum relevance(mRMR)feature selection methods were analyzed to acquire the optimized characteristics,and various classifiers were trained using the fused features.Open-source color image datasets(KVASIR,NERTHUS,and stomach ULCER)were used for performance evaluation.The study revealed that the subspace discriminant classifier provided an efficient result with 95.02%accuracy on the KVASIR dataset,which proved to be better than the existing state-of-the-art approaches. | Muhammad Ramzan Mudassar Raza Muhammad Sharif Muhammad Attique Khan Yunyoung Nam | 2021 | Computers, Materials & Continua2021，，12： | 1 |
| 11 | 3D Head Pose Estimation through Facial Features and Deep Convolutional Neural Networks显示文摘Face image analysis is one among several important cues in computer vision.Over the last five decades,methods for face analysis have received immense attention due to large scale applications in various face analysis tasks.Face parsing strongly benefits various human face image analysis tasks inducing face pose estimation.In this paper we propose a 3D head pose estimation framework developed through a prior end to end deep face parsing model.We have developed an end to end face parts segmentation framework through deep convolutional neural networks(DCNNs).For training a deep face parts parsing model,we label face images for seven different classes,including eyes,brows,nose,hair,mouth,skin,and back.We extract features from gray scale images by using DCNNs.We train a classifier using the extracted features.We use the probabilistic classification method to produce gray scale images in the form of probability maps for each dense semantic class.We use a next stage of DCNNs and extract features from grayscale images created as probability maps during the segmentation phase.We assess the performance of our newly proposed model on four standard head pose datasets,including Pointing’04,Annotated Facial Landmarks in the Wild(AFLW),Boston University(BU),and ICT-3DHP,obtaining superior results as compared to previous results. | Khalil Khan Jehad Ali Kashif Ahmad Asma Gul Ghulam Sarwar Sahib Khan Qui Thanh Hoai Ta Tae-Sun Chung Muhammad Attique | 2021 | Computers, Materials & Continua2021，，2： | 1 |
| 12 | Weapons Detection for Security and Video Surveillance Using CNN and YOLO-V5s显示文摘In recent years,the number of Gun-related incidents has crossed over 250,000 per year and over 85%of the existing 1 billion firearms are in civilian hands,manual monitoring has not proven effective in detecting firearms.which is why an automated weapon detection system is needed.Various automated convolutional neural networks(CNN)weapon detection systems have been proposed in the past to generate good results.However,These techniques have high computation overhead and are slow to provide real-time detection which is essential for the weapon detection system.These models have a high rate of false negatives because they often fail to detect the guns due to the low quality and visibility issues of surveillance videos.This research work aims to minimize the rate of false negatives and false positives in weapon detection while keeping the speed of detection as a key parameter.The proposed framework is based on You Only Look Once(YOLO)and Area of Interest(AOI).Initially,themodels take pre-processed frames where the background is removed by the use of the Gaussian blur algorithm.The proposed architecture will be assessed through various performance parameters such as False Negative,False Positive,precision,recall rate,and F1 score.The results of this research work make it clear that due to YOLO-v5s high recall rate and speed of detection are achieved.Speed reached 0.010 s per frame compared to the 0.17 s of the Faster R-CNN.It is promising to be used in the field of security and weapon detection. | Abdul Hanan Ashraf Muhammad Imran Abdulrahman M.Qahtani Abdulmajeed Alsufyani Omar Almutiry Awais Mahmood Muhammad Attique Mohamed Habib | 2022 | Computers, Materials & Continua2022，，2： | 1 |
| 13 | Automatic Heart Disease Detection by Classification of Ventricular Arrhythmias on ECG Using Machine Learning显示文摘This paper focuses on detecting diseased signals and arrhythmias classification into two classes:ventricular tachycardia and premature ventricular contraction.The sole purpose of the signal detection is used to determine if a signal has been collected from a healthy or sick person.The proposed research approach presents a mathematical model for the signal detector based on calculating the instantaneous frequency(IF).Once a signal taken from a patient is detected,then the classifier takes that signal as input and classifies the target disease by predicting the class label.While applying the classifier,templates are designed separately for ventricular tachycardia and premature ventricular contraction.Similarities of a given signal with both the templates are computed in the spectral domain.The empirical analysis reveals precisions for the detector and the applied classifier are 100%and 77.27%,respectively.Moreover,instantaneous frequency analysis provides a benchmark that IF of a normal signal ranges from 0.8 to 1.1 Hz whereas IF range for ventricular tachycardia and premature ventricular contraction is 0.08–0.6 Hz.This indicates a serious loss of high-frequency contents in the spectrum,implying that the heart’s overall activity is slowed down.This study may help medical practitioners in detecting the heart disease type based on signal analysis. | Khalid Mahmood Aamir Muhammad Ramzan Saima Skinadar Hikmat Ullah Khan Usman Tariq Hyunsoo Lee Yunyoung Nam Muhammad Attique Khan | 2022 | Computers, Materials & Continua2022，，4： | 1 |
| 14 | VISPNN:VGG-Inspired Stochastic Pooling Neural Network显示文摘Aim Alcoholism is a disease that a patient becomes dependent or addicted to alcohol.This paper aims to design a novel artificial intelligence model that can recognize alcoholism more accurately.Methods We propose the VGG-Inspired stochastic pooling neural network(VISPNN)model based on three components:(i)a VGG-inspired mainstay network,(ii)the stochastic pooling technique,which aims to outperform traditional max pooling and average pooling,and(iii)an improved 20-way data augmentation(Gaussian noise,salt-and-pepper noise,speckle noise,Poisson noise,horizontal shear,vertical shear,rotation,Gamma correction,random translation,and scaling on both raw image and its horizontally mirrored image).In addition,two networks(Net-I and Net-II)are proposed in ablation studies.Net-I is based on VISPNN by replacing stochastic pooling with ordinary max pooling.Net-II removes the 20-way data augmentation.Results The results by ten runs of 10-fold cross-validation show that our VISPNN model gains a sensitivity of 97.98±1.32,a specificity of 97.80±1.35,a precision of 97.78±1.35,an accuracy of 97.89±1.11,an F1 score of 97.87±1.12,an MCC of 95.79±2.22,an FMI of 97.88±1.12,and an AUC of 0.9849,respectively.Conclusion The performance of our VISPNN model is better than two internal networks(Net-I and Net-II)and ten state-of-the-art alcoholism recognition methods. | Shui-Hua Wang Muhammad Attique Khan Yu-Dong Zhang | 2022 | Computers, Materials & Continua2022，，2： | 1 |
| 15 | Skin Lesion Segmentation and Classification Using Conventional and Deep Learning Based Framework显示文摘Background:In medical image analysis,the diagnosis of skin lesions remains a challenging task.Skin lesion is a common type of skin cancer that exists worldwide.Dermoscopy is one of the latest technologies used for the diagnosis of skin cancer.Challenges:Many computerized methods have been introduced in the literature to classify skin cancers.However,challenges remain such as imbalanced datasets,low contrast lesions,and the extraction of irrelevant or redundant features.Proposed Work:In this study,a new technique is proposed based on the conventional and deep learning framework.The proposed framework consists of two major tasks:lesion segmentation and classification.In the lesion segmentation task,contrast is initially improved by the fusion of two filtering techniques and then performed a color transformation to color lesion area color discrimination.Subsequently,the best channel is selected and the lesion map is computed,which is further converted into a binary form using a thresholding function.In the lesion classification task,two pre-trained CNN models were modified and trained using transfer learning.Deep features were extracted from both models and fused using canonical correlation analysis.During the fusion process,a few redundant features were also added,lowering classification accuracy.A new technique called maximum entropy score-based selection(MESbS)is proposed as a solution to this issue.The features selected through this approach are fed into a cubic support vector machine(C-SVM)for the final classification.Results:The experimental process was conducted on two datasets:ISIC 2017 and HAM10000.The ISIC 2017 dataset was used for the lesion segmentation task,whereas the HAM10000 dataset was used for the classification task.The achieved accuracy for both datasets was 95.6% and 96.7%, respectively, which was higher thanthe existing techniques. | Amina Bibi Muhamamd Attique Khan Muhammad Younus Javed Usman Tariq Byeong-Gwon Kang Yunyoung Nam Reham R.Mostafa Rasha H.Sakr | 2022 | Computers, Materials & Continua2022，，5： | 1 |
| 16 | Elimination of Citrus tristeza closterovirus from citrus bud- wood through thermotherapy显示文摘 | MUHAMMAD A MUHAMMAD I ATTIQUE A | 2005 | Pak J Bot2005，37，2： | 1 |
| 17 | Traffic Management in Internet of Vehicles Using Improved Ant Colony Optimization显示文摘The Internet of Vehicles(IoV)is a networking paradigm related to the intercommunication of vehicles using a network.In a dynamic network,one of the key challenges in IoV is traffic management under increasing vehicles to avoid congestion.Therefore,optimal path selection to route traffic between the origin and destination is vital.This research proposed a realistic strategy to reduce traffic management service response time by enabling real-time content distribution in IoV systems using heterogeneous network access.Firstly,this work proposed a novel use of the Ant Colony Optimization(ACO)algorithm and formulated the path planning optimization problem as an Integer Linear Program(ILP).This integrates the future estimation metric to predict the future arrivals of the vehicles,searching the optimal routes.Considering the mobile nature of IOV,fuzzy logic is used for congestion level estimation along with the ACO to determine the optimal path.The model results indicate that the suggested scheme outperforms the existing state-of-the-art methods by identifying the shortest and most cost-effective path.Thus,this work strongly supports its use in applications having stringent Quality of Service(QoS)requirements for the vehicles. | Abida Sharif Imran Sharif Muhammad Asim Saleem Muhammad Attique Khan Majed Alhaisoni Marriam Nawaz Abdullah Alqahtani Ye Jin Kim Byoungchol Chang | 2023 | Computers, Materials & Continua2023，，6： | 1 |
| 18 | Importance of Features Selection,Attributes Selection,Challenges and Future Directions for Medical Imaging Data:A Review显示文摘In the area of pattern recognition and machine learning,features play a key role in prediction.The famous applications of features are medical imaging,image classification,and name a few more.With the exponential growth of information investments in medical data repositories and health service provision,medical institutions are collecting large volumes of data.These data repositories contain details information essential to support medical diagnostic decisions and also improve patient care quality.On the other hand,this growth also made it difficult to comprehend and utilize data for various purposes.The results of imaging data can become biased because of extraneous features present in larger datasets.Feature selection gives a chance to decrease the number of components in such large datasets.Through selection techniques,ousting the unimportant features and selecting a subset of components that produces prevalent characterization precision.The correct decision to find a good attribute produces a precise grouping model,which enhances learning pace and forecast control.This paper presents a review of feature selection techniques and attributes selection measures for medical imaging.This review is meant to describe feature selection techniques in a medical domainwith their pros and cons and to signify its application in imaging data and data mining algorithms.The review reveals the shortcomings of the existing feature and attributes selection techniques to multi-sourced data.Moreover,this review provides the importance of feature selection for correct classification of medical infections.In the end,critical analysis and future directions are provided. | Nazish Naheed Muhammad Shaheen Sajid Ali Khan Mohammed Alawairdhi Muhammad Attique Khan | 2020 | Computer Modeling in Engineering & Sciences2020，，10： | 1 |
| 19 | Dynamic Hand Gesture Recognition Using 3D-CNN and LSTM Networks显示文摘Recognition of dynamic hand gestures in real-time is a difficult task because the system can never know when or from where the gesture starts and ends in a video stream.Many researchers have been working on visionbased gesture recognition due to its various applications.This paper proposes a deep learning architecture based on the combination of a 3D Convolutional Neural Network(3D-CNN)and a Long Short-Term Memory(LSTM)network.The proposed architecture extracts spatial-temporal information from video sequences input while avoiding extensive computation.The 3D-CNN is used for the extraction of spectral and spatial features which are then given to the LSTM network through which classification is carried out.The proposed model is a light-weight architecture with only 3.7 million training parameters.The model has been evaluated on 15 classes from the 20BN-jester dataset available publicly.The model was trained on 2000 video-clips per class which were separated into 80%training and 20%validation sets.An accuracy of 99%and 97%was achieved on training and testing data,respectively.We further show that the combination of 3D-CNN with LSTM gives superior results as compared to MobileNetv2+LSTM. | Muneeb Ur Rehman Fawad Ahmed Muhammad Attique Khan Usman Tariq Faisal Abdulaziz Alfouzan Nouf M.Alzahrani Jawad Ahmad | 2022 | Computers, Materials & Continua2022，，3： | 0 |
| 20 | An Artificial Intelligence-Based Framework for Fruits Disease Recognition Using Deep Learning显示文摘Fruit infections have an impact on both the yield and the quality of the crop.As a result,an automated recognition system for fruit leaf diseases is important.In artificial intelligence(AI)applications,especially in agriculture,deep learning shows promising disease detection and classification results.The recent AI-based techniques have a few challenges for fruit disease recognition,such as low-resolution images,small datasets for learning models,and irrelevant feature extraction.This work proposed a new fruit leaf leaf leaf disease recognition framework using deep learning features and improved pathfinder optimization.Three fruit types have been employed in this work for the validation process,such as apple,grape,and Citrus.In the first step,a noisy dataset is prepared by employing the original images to learn the designed framework better.The EfficientNet-B0 deep model is fine-tuned on the next step and trained separately on the original and noisy data.After that,features are fused using a serial concatenation approach that is later optimized in the next step using an improved Path Finder Algorithm(PFA).This algorithm aims to select the best features based on the fitness score and ignore redundant information.The selected features are finally classified using machine learning classifiers such as Medium Neural Network,Wide Neural Network,and Support Vector Machine.The experimental process was conducted on each fruit dataset separately and obtained an accuracy of 100%,99.7%,99.7%,and 93.4%for apple,grape,Citrus fruit,and citrus plant leaves,respectively.A detailed analysis is conducted and also compared with the recent techniques,and the proposed framework shows improved accuracy. | Irfan Haider Muhammad Attique Khan Muhammad Nazir Taerang Kim Jae-Hyuk Cha | 2024 | Computer Systems Science & Engineering2024，48，2： | 0 |
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